R统计学1 - 基础操作入门问题1-20

R统计学入门基础问题

1. 如何生成100个高斯（正态）分布随机数

x <- rnorm(100, mean = 5, sd = 0.1)
x
# [1] 4.893534 5.046611 5.081097 4.979164 5.181700 5.038192 5.135376 5.173346 4.968877 4.986146
# [11] 4.946258 5.198199 5.055531 4.943075 5.073673 4.831518 5.020880 4.930038 5.069399 4.966239
# [21] 5.035919 4.748342 4.950412 5.098074 5.200755 5.016067 4.980694 5.092845 4.969212 5.166748
# [31] 5.063731 5.188161 5.067183 4.832255 4.948366 4.945234 5.281972 5.004589 5.052194 4.970655
# [41] 4.821950 5.172387 5.075971 4.881912 5.077742 4.959202 4.978079 5.071156 5.122046 5.099949
# [51] 5.056486 5.073163 4.986136 4.879002 5.035217 5.115133 5.170419 4.979108 4.999929 5.104167
# [61] 4.995740 5.136457 5.088393 5.068214 4.979342 4.862489 4.984418 4.851406 5.101475 5.009014
# [71] 4.955209 5.113826 4.993114 4.969778 5.174806 5.118025 4.940426 5.016818 5.008397 5.007662
# [81] 5.037553 4.841561 4.927540 4.792235 4.943234 5.025745 4.877301 5.006027 5.105440 4.816338
# [91] 5.078376 5.099721 4.976009 4.844325 4.920226 5.052165 5.042234 4.876854 5.053872 5.029314
mean(x)
# 5.015125
sd(x)
# 0.1027691
summary(x)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
# 4.748   4.954   5.016   5.015   5.079   5.282 

2. 如何清除内存所有变量

rm(list = ls(all = TRUE))

3. 如何更改小数显示数字位数

pi <- 3.1415926
# digits支持1-22个数字，默认为7
options(digits = 4) 
pi
# 3.142

4. 如何通过R调用系统内程序

使用system()或shell.exec()函数启动程序

system(paste("C:/Program Files/Internet Explorer/iexplore.exe" ,"cran.r-project.org") , wait = FALSE)
system("notepad")

5. 如果本地升级R，不重装R包

其他目录安装R新版本，将就版本R程序的library目录下的R包复制至新的R library目录下，执行以下代码

update.packages()

6. 如何获取当前工作目录和设置工作目录

# 获取当前working directory
getwd()

# 设置工作目录
setwd("目录路径")

7. 如何保存当前工作

# 工作目录生成.RData文件
save.image()

# 保存R对象
pi <- 3.1415926
save(pi , file = "pi.data")

8. 如何获取本地安装的R包列表

.packages(all.available = TRUE)
# [1] "abind"                          "ade4"                          
# [3] "afex"                           "affy"                          
# [5] "affyio"                         "airway"                        
# [7] "ALL"                            "Amelia" 
......

9. 如何查看函数的代码

summary

# 查看类函数方法
methods(summary)
# [1] summary,ANY-method                             summary,CompressedIRangesList-method          
# [3] summary,DBIObject-method                       summary,FilterResults-method                  
# [5] summary,FilterRules-method                     summary,Hits-method

# 查看类函数lm方法代码
summary.lm
# function (object, correlation = FALSE, symbolic.cor = FALSE, 
#           ...) 
# {
#   z <- object
#   p <- z$rank
#   rdf <- z$df.residual
#   if (p == 0) {
#     r <- z$residuals
#     n <- length(r)
#     w <- z$weights
#     if (is.null(w)) {
#       rss <- sum(r^2)
#     }
#     else {
#       rss <- sum(w * r^2)
#       r <- sqrt(w) * r
#     }
#     resvar <- rss/rdf
#     ans <- z[c("call", "terms", if (!is.null(z$weights)) "weights")]
#     class(ans) <- "summary.lm"
#     ans$aliased <- is.na(coef(object))
#     ans$residuals <- r
#     ans$df <- c(0L, n, length(ans$aliased))
#     ans$coefficients <- matrix(NA_real_, 0L, 4L, dimnames = list(NULL, 
#                                                                  c("Estimate", "Std. Error", "t value", "Pr(>|t|)")))
#     ans$sigma <- sqrt(resvar)
#     ans$r.squared <- ans$adj.r.squared <- 0
#     ans$cov.unscaled <- matrix(NA_real_, 0L, 0L)
#     if (correlation) 
#       ans$correlation <- ans$cov.unscaled
#     return(ans)
#   }
#   if (is.null(z$terms)) 
#     stop("invalid 'lm' object:  no 'terms' component")
#   if (!inherits(object, "lm")) 
#     warning("calling summary.lm() ...")
#   Qr <- qr.lm(object)
#   n <- NROW(Qr$qr)
#   if (is.na(z$df.residual) || n - p != z$df.residual) 
#     warning("residual degrees of freedom in object suggest this is not an \"lm\" fit")
#   r <- z$residuals
#   f <- z$fitted.values
#   w <- z$weights
#   if (is.null(w)) {
#     mss <- if (attr(z$terms, "intercept")) 
#       sum((f - mean(f))^2)
#     else sum(f^2)
#     rss <- sum(r^2)
#   }
#   else {
#     mss <- if (attr(z$terms, "intercept")) {
#       m <- sum(w * f/sum(w))
#       sum(w * (f - m)^2)
#     }
#     else sum(w * f^2)
#     rss <- sum(w * r^2)
#     r <- sqrt(w) * r
#   }
#   resvar <- rss/rdf
#   if (is.finite(resvar) && resvar < (mean(f)^2 + var(c(f))) * 
#       1e-30) 
#     warning("essentially perfect fit: summary may be unreliable")
#   p1 <- 1L:p
#   R <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
#   se <- sqrt(diag(R) * resvar)
#   est <- z$coefficients[Qr$pivot[p1]]
#   tval <- est/se
#   ans <- z[c("call", "terms", if (!is.null(z$weights)) "weights")]
#   ans$residuals <- r
#   ans$coefficients <- cbind(Estimate = est, `Std. Error` = se, 
#                             `t value` = tval, `Pr(>|t|)` = 2 * pt(abs(tval), rdf, 
#                                                                   lower.tail = FALSE))
#   ans$aliased <- is.na(z$coefficients)
#   ans$sigma <- sqrt(resvar)
#   ans$df <- c(p, rdf, NCOL(Qr$qr))
#   if (p != attr(z$terms, "intercept")) {
#     df.int <- if (attr(z$terms, "intercept")) 
#       1L
#     else 0L
#     ans$r.squared <- mss/(mss + rss)
#     ans$adj.r.squared <- 1 - (1 - ans$r.squared) * ((n - 
#                                                        df.int)/rdf)
#     ans$fstatistic <- c(value = (mss/(p - df.int))/resvar, 
#                         numdf = p - df.int, dendf = rdf)
#   }
#   else ans$r.squared <- ans$adj.r.squared <- 0
#   ans$cov.unscaled <- R
#   dimnames(ans$cov.unscaled) <- dimnames(ans$coefficients)[c(1, 
#                                                              1)]
#   if (correlation) {
#     ans$correlation <- (R * resvar)/outer(se, se)
#     dimnames(ans$correlation) <- dimnames(ans$cov.unscaled)
#     ans$symbolic.cor <- symbolic.cor
#   }
#   if (!is.null(z$na.action)) 
#     ans$na.action <- z$na.action
#   class(ans) <- "summary.lm"
#   ans
# }

10. 如何在R中使用科学计数法

1e10 == 10000000000
# TRUE

1.2e-4 == 0.00012
# TRUE

10. 如何在R中读取其他统计软件输入数据

使用 foreign 包，它可以读取 Minitab, S, SAS, SPSS, Stata, Systat, dBase 保存的数据。

foreign::read.spss()
foreign::read.S()
foreign::read.systat()

11. R如何读取excel数据

使用 foreign 包，它可以读取 Minitab, S, SAS, SPSS, Stata, Systat, dBase 保存的数据。

# 方法一，将excel数据保存为.csv文件
data <- read.csv("data.csv")

# 方法二，使用xlsReadWrite R包
data <- read.xls("data.csv")

# 方法三，使用RODBC包
library(RODBC)
file <− odbcConnectExcel ("rexceltest.xls")
data <− sqlFetch(file , "Sheet1")
close(file )

12. R如何快速保存输出结果至文本文件

使用sink()函数

data("mtcars")
sink(file = "mtcars.txt")

13. R如何将因子(factor)转换为数字

# f为因子对象
as.numeric(as.character(f))
# 或
as.numeric(levels(f))[as.integer(f)]

14. R如何可视化输入数据

打开可视化界面

data <- data.frame()
edit(data) # 或fix(data

15. R如何连接数据库读取数据

R支持基于SQL语言的关系数据库，RMySQL包提供MySQL数据库接口，RODBC包提供多种数据库接口（标准ODBC接口数据库）。经测试，Windows 平台上的 Microsoft SQL Server、Access、Oracle、MySQL、PostgreSQL，Linux 平台上的 MySQL、Oracle、PostgreSQL、SQLite 都有实际应用案例。

library(RODBC)
# 连接数据库
db <- odbcConnect("database_name", uid = "username", pwd = "password")
# 查询
stocks <- sqlQuery(db, "select * from quotes")
# 断开数据库连接
odbcClose(db)

16. R如何在一个变量中追加元素

x <- 1:10
x
# 1  2  3  4  5  6  7  8  9 10

# 元素1后面追加元素0
append(x, 0, after = 1)
# 1  0  2  3  4  5  6  7  8  9 10

# 元素5后面追加元素100
append(x, 100, after = 5)
# 1   2   3   4   5 100   6   7   8   9  10

17. R如何删除某行或某列数据

逗号在前删除行，逗号在后删除列，前面添加负号-

# 创建4x5矩阵
x <- data.frame(matrix(1:20, nrow = 4, byrow = 5))
x
#  X1 X2 X3 X4 X5
#1  1  2  3  4  5
#2  6  7  8  9 10
#3 11 12 13 14 15
#4 16 17 18 19 20

# 删除第1和第3行数据
x1 <- x[-c(1,3) ,]
x1
#  X2 X4 X5
#1  2  4  5
#2  7  9 10
#3 12 14 15
#4 17 19 20

# 删除第1和第3列数据
x2 <- x[, -c(1,3)]
x2
#  X2 X4 X5
#1  2  4  5
#2  7  9 10
#3 12 14 15
# 17 19 20

18. R如何判断dataframe是否相同

# 创建dataframe
d1 <- data.frame(num = 1:8, lib = letters[1:8])
d1
# num lib
# 1   1   a
# 2   2   b
# 3   3   c
# 4   4   d
# 5   5   e
# 6   6   f
# 7   7   g
# 8   8   h

d2 <- d1
# 将d2第3行第1列数值和第8行第2列数值赋值为2
d2[[3,1]] <- 2 -> d2[[8, 2]]
d2
# num lib
# 1   1   a
# 2   2   b
# 3   2   c
# 4   4   d
# 5   5   e
# 6   6   f
# 7   7   g
# 8   8   2

any(d1==d2)
# TRUE, 至少1个值相同返回为TRUE

all(d1==d2)
# FALSE， 全部值相同返回为TRUE

# 获取2个dataframe不相同元素位置
which(d1 != d2, arr.ind = TRUE)
# row col
# [1,]   3   1
# [2,]   8   2

19. R如何去除重复元素

使用unique()或duplicated()函数

a1 <- c(1:20, 1:5, 3:6)
a1
# 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20  1  2  3  4  5  3  4  5  6
unique(a1) 或 x <- x[!duplicated(x)]
# 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

20. R如何删除list中元素

lst <- list("l1" = list("a"=1, "b"=2), "l2" = list("a"=1, "b"=2))
lst
# $l1
# $l1$a
# [1] 1
# 
# $l1$b
# [1] 2
# 
# 
# $l2
# $l2$a
# [1] 1
# 
# $l2$b
# [1] 2

# 删除l1中b元素
lst[["l1"]]["b"] <- NULL
# lst
# $l1
# $l1$a
# [1] 1
# 
# 
# $l2
# $l2$a
# [1] 1
# 
# $l2$b
# [1] 2